How do stock price indices absorb the COVID-19 pandemic shocks?

Zhang, Xu; Ding, Zhijing; Hang, Jianqin; He, Qizhi

doi:10.1016/j.najef.2022.101672

Cited by 43 publications

(22 citation statements)

References 30 publications

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“…The rapid growth of the tourism economy and decreasing ESV indicate the unsynchronized and uncoordinated development between the two systems; thus, the coupling coordination degree became mild disorder afterward. This inference is consistent with the discussions of previous studies [ 16 , 35 , 40 , 59 ]. In addition, there were spatial and temporal differences in the coupling coordination degree of the tourism economy and ESV among the five cities in southern Jiangsu, and it could be generated by a variety of factors such as economic development level, tourism resources and transportation infrastructure.…”

Section: Discussionsupporting

confidence: 94%

Coupling and Coordination Relationship between the Tourism Economy and Ecosystem Service Value in Southern Jiangsu, China

Wang

Li³

2022

IJERPH

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The relationship between the tourism economy and the ecosystem service value (ESV) is crucial for sustainable regional development. This study takes southern Jiangsu as a research object. Firstly, the development level of the tourism economy and ecosystem service value in southern Jiangsu from 2000 to 2020 are evaluated with the entropy method, ecosystem service value is estimated and the dynamic degree of land use is computed. Secondly, the coupling coordination degree model is used to explore the coupling coordination degree between the two systems. Finally, the interaction mechanism between the tourism economy and ecosystem service function is elaborated. The result shows that: (1) There are disparities in the levels of a comprehensive tourism economy in different cities, and the overall development level of the tourism economy in southern Jiangsu shows a cyclical fluctuation pattern. (2) Spatial variation of ecosystem service value exists in different cities in southern Jiangsu, with an overall trend of increasing in the beginning followed by a decline. (3) The coupling coordination degree between the tourism economic system and ecosystem service functions in southern Jiangsu demonstrates an inverted U-shaped development pattern from 2000 to 2020, evolving from mild disorder to intermediate coordination and then back to mild disorder, and the development of two subsystems is unstable and imbalanced. Within the region, Nanjing, Suzhou and Zhenjiang have experienced a rise in coupling coordination degree followed by a decline. This study also reveals the coupling mechanism between ecological service functions and the tourism economic system, and provides suggestions for ecological preservation and sustainable development of tourism industry in southern Jiangsu. This research can be a reference for tourism and regional development in southern Jiangsu and the whole Yangtze Delta region.

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Section: Discussionsupporting

confidence: 94%

Coupling and Coordination Relationship between the Tourism Economy and Ecosystem Service Value in Southern Jiangsu, China

Wang

Li³

2022

IJERPH

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“…It is more interpretable to humans to quantify the impact of both environment and the manufacture itself. The special events such as COVID-19 pandemic have brought great to the price volatility [6], and further, the price volatility will influence the supply chain. Time series forecasting reveals future trends through the historical data of the time series, i.e., the seasonal cycle can be obtained through time series forecasting thus facilitating the development of the length of time for which the ordering strategy applies similarly to the method which Benvenuto et al used [7].…”

Section: Methodsmentioning

confidence: 99%

“…To minimize the ordering cost, the quantities of raw materials A, B, and C ordered are set to x, y, and z. Since in practice, the unit prices of raw materials A and B are 20% and 10% higher than those of raw materials C, respectively, and the price of C is set to be the unit price, this optimization objective can be expressed as (6).…”

Section: Establishment Of Flexible Constraints For Multiobjectivementioning

confidence: 99%

The Optimal Supply Decision Based on Dynamic Multiobjective Optimization and Prediction

Cheng

Zhou

et al. 2022

Journal of Function Spaces

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Different material supply-related decisions intensively affect the efficiency of manufacturers. To obtain a suitable supply-related protocol, this study proposes a supply selection model which considers both manufacturers’ development orientation and material ordering. In contrast to traditional approaches that rely on expert opinions, the proposed approach in this study allows the time series analysis (ARIMA) to forecast the trend in manufacturers’ development during the execution of the plan. Based on the predicted trend, taking the minimum of total material management cost as the objective function, the control function and optimization conditions are constructed to select the appropriate protocol. The dynamic prediction protocol is obtained by considering the variation in production and material costs by an evolutionary algorithm. The model enables users to determine material supply protocol in continuous time and autonomously adapt to changes in the manufacturers’ production goals within a lower convergence time.

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“…There are the rapidly growing body of literature on the financial market and social economics reaction to COVID-19. The stock market’s reaction to COVID-19 is first paid closely attentions, and is found that the large risk of stock market is formed during the COVID-19 pandemic, see e.g., Ashraf (2020) , Baker, Bloom, Davis, et al (2020) , Cox, Greenwald, and Ludvigson (2020) , Liu, Huynh, and Dai (2021) , Duan, Liu, and Wang (2021) , Li et al, 2021a , Li et al, 2021b , Rehman, Kang, Ahmad, et al (2021) , and Zhang, Ding, Hang, et al (2022) . The reaction of the bond market to COVID-19 is another perspective and has been paid closely attentions, and is found that the bond spread and liquidity are deteriorated clearly, and the extreme liquidity pressure has been caused by the financing constraint which is from different financial institutions or intermediaries (e.g., see Apergis et al, 2022 , Falato et al, 2021 , Haddad et al, 2021 , Paczos and Shakhnov, 2022 ).…”

Section: Introductionsmentioning

confidence: 99%